I'm building product search with keyword plus vector matching, what database should I choose?

If you want keyword search + vector similarity search in one system, the best database depends on your scale and how much search quality you need.

Good default choice

PostgreSQL + pgvector

Choose this if:

• you’re building an MVP or early product
• your catalog is small to medium
• you want the simplest stack
• you already use Postgres

Pros

• single database for relational data, keyword search, and vectors
• easy to operate compared with a separate search engine
• supports hybrid ranking via SQL
• great for filters like brand, price, category, availability

Cons

• keyword search is decent, but not as advanced as dedicated search engines
• vector performance is good, but not the best at very large scale

Best for

• tens of thousands to a few million products
• search with strong filtering and decent hybrid relevance

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Best search-first choice

Elasticsearch / OpenSearch

Choose this if:

• search relevance is a core product feature
• you need advanced keyword search, faceting, autocomplete, typo tolerance
• you want mature hybrid search support

Pros

• excellent keyword search
• great filtering, faceting, analyzers, synonyms
• can combine lexical and vector search
• built for search at scale

Cons

• more operational overhead
• more moving parts than Postgres
• vector search is improving, but hybrid relevance tuning can take work

Best for

• large catalogs
• e-commerce search where quality matters a lot
• teams comfortable with search infrastructure

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Best fully managed vector + keyword option

MongoDB Atlas Search / Atlas Vector Search

Choose this if:

• you already use MongoDB
• you want managed search without running separate infra
• your product data is document-shaped

Pros

• integrated search and vector capabilities
• easy managed experience
• good for product documents

Cons

• less flexible than a dedicated search engine in some cases
• may not be ideal if SQL/relational queries are important

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Other options

Pinecone / Weaviate / Qdrant

These are great if you mainly want vector search, but for keyword + vector product search, they usually need to be paired with another system for lexical search and structured filtering.

• Qdrant: strong vector DB, good filtering, open source
• Weaviate: hybrid search friendly, good developer experience
• Pinecone: managed vector search, easiest ops, but keyword search is not the main strength

These are best when semantic search is central and keyword search is secondary.

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Practical recommendation

Pick based on your stage:

• MVP / small-medium catalog → PostgreSQL + pgvector
• Serious search relevance / large catalog → Elasticsearch or OpenSearch
• Already on MongoDB → MongoDB Atlas Search
• Mostly semantic search → Qdrant / Weaviate / Pinecone

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My default suggestion

If you’re unsure, start with:

PostgreSQL + pgvector

• store product metadata in Postgres
• use full-text search for keywords
• use pgvector for embeddings
• combine both in a hybrid ranking query

This is usually the fastest path to a working product search system.

If the search experience becomes a core differentiator later, you can move to OpenSearch/Elasticsearch.

If you want, I can also give you:

1. a decision matrix,
2. an architecture diagram, or
3. a sample hybrid search query for Postgres/OpenSearch.